{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".idea\n",
      ".ipynb_checkpoints\n",
      "change2.csv\n",
      "choose_model.ipynb\n",
      "feature_sd.csv\n",
      "jupyter27_x64.bat\n",
      "modelR.py\n",
      "phone_dict.pickle\n"
     ]
    }
   ],
   "source": [
    "#coding=utf-8\n",
    "#python2\n",
    "import pandas as pd\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import *\n",
    "mpl.rcParams['font.sans-serif']=['FangSong']\n",
    "pd.set_option('display.width', 320)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.set_option('float_format','{:20,.2f}'.format)\n",
    "from os import listdir\n",
    "for d in listdir(\".\"):\n",
    "    print d.decode(\"gbk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 621)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>is_buy</th>\n",
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       "      <th>b_1</th>\n",
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       "      <th>b_103</th>\n",
       "      <th>b_104</th>\n",
       "      <th>b_105</th>\n",
       "      <th>...</th>\n",
       "      <th>p_18</th>\n",
       "      <th>p_19</th>\n",
       "      <th>p_2</th>\n",
       "      <th>p_3</th>\n",
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       "      <th>p_7</th>\n",
       "      <th>p_8</th>\n",
       "      <th>p_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 621 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             is_buy  b_0  b_1  b_10  b_100  b_101  b_102  b_103  b_104  b_105 ...   p_18  p_19  p_2  p_3  p_4  p_5  p_6  p_7  p_8  p_9\n",
       "user                                                                          ...                                                     \n",
       "13356614239  0       0    0    0     0      0      0      1      0      0     ...   1     2     1    1    1    1    1    1    1    1  \n",
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       "18963025304  1       0    0    0     0      0      0      1      0      0     ...   0     0     0    0    0    0    0    0    0    0  \n",
       "\n",
       "[5 rows x 621 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"feature_sd.csv\",encoding=\"utf-8\")\n",
    "data.set_index(\"user\",inplace=True)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <td>...</td>\n",
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       "</table>\n",
       "<p>5 rows × 620 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             b_0  b_1  b_10  b_100  b_101  b_102  b_103  b_104  b_105  b_106 ...   p_18  p_19  p_2  p_3  p_4  p_5  p_6  p_7  p_8  p_9\n",
       "user                                                                         ...                                                     \n",
       "13356614239  0    0    0     0      0      0      1      0      0      0     ...   1     2     1    1    1    1    1    1    1    1  \n",
       "18006398880  0    0    0     0      0      0      0      0      0      0     ...   0     0     3    3    3    3    3    3    0    0  \n",
       "15376012176  1    0    0     0      0      0      0      1      0      0     ...   0     0     0    0    0    0    0    0    0    0  \n",
       "15318795806  0    0    0     0      0      0      0      0      0      0     ...   4     4     3    3    3    3    3    3    3    3  \n",
       "18963025304  0    0    0     0      0      0      1      0      0      0     ...   0     0     0    0    0    0    0    0    0    0  \n",
       "\n",
       "[5 rows x 620 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data[\"is_buy\"]\n",
    "X=data.drop(\"is_buy\",axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "model=Ridge()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1OouBPV3wd7czdkhCGERVfTVR6VvZnrUDnaIj2CGAaYGTcLOWNRdExyHN8qJF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\nhEgy/x/Fs47zi54JuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121192320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "import numpy as np\n",
    "ts,rs,ps,f1s=[],[],[],[]\n",
    "for t in np.arange(0,1.1,0.05):\n",
    "    threshold=t\n",
    "    predict=[1 if p>threshold else 0 for p in predict_con]\n",
    "#     print accuracy_score(y_test,predict)\n",
    "    cm=confusion_matrix(y_test,predict,labels=[0,1])\n",
    "#     print cm\n",
    "    recall1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "    precision1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "    f11=2*recall1*precision1/(recall1+precision1)\n",
    "#     print threshold,recall1,precision1,f11\n",
    "    ts.append(threshold)\n",
    "    rs.append(recall1)\n",
    "    ps.append(precision1)\n",
    "    f1s.append(f11)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.plot(ts,rs,label=\"recall1\")\n",
    "plt.plot(ts,ps,label=\"precision1\")\n",
    "plt.plot(ts,f1s,label=\"f11\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.text(0.4,0.9,\"Ridge Regression(Class 1)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "model=Lasso(0.1)\n",
    "model.fit(X_train,y_train)\n",
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6YbeabrErl+v0QlwKfb2eQkMxhfpiivTFFBqKKTKUUKQvRm88dcCrm9qVKI9w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\noY3NZjMvJq8FYHbfiUR6hbGvdD+b8rd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jhum5zJqYhUEfvuPgfcnY2+XGceAAbR9tx3m4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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1406857b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "import numpy as np\n",
    "ts,rs,ps,f1s=[],[],[],[]\n",
    "for t in np.arange(0.3,0.5,0.005):\n",
    "    threshold=t\n",
    "    predict=[1 if p>threshold else 0 for p in predict_con]\n",
    "#     print accuracy_score(y_test,predict)\n",
    "    cm=confusion_matrix(y_test,predict,labels=[0,1])\n",
    "#     print cm\n",
    "    recall1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "    precision1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "    f11=2*recall1*precision1/(recall1+precision1)\n",
    "#     print threshold,recall1,precision1,f11\n",
    "    ts.append(threshold)\n",
    "    rs.append(recall1)\n",
    "    ps.append(precision1)\n",
    "    f1s.append(f11)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.plot(ts,rs,label=\"recall1\")\n",
    "plt.plot(ts,ps,label=\"precision1\")\n",
    "plt.plot(ts,f1s,label=\"f11\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.text(0.3,0.9,\"Lasso Regression(Class 1,alpha=0.1)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n",
       "      max_iter=1000, normalize=False, positive=False, precompute=False,\n",
       "      random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "model=ElasticNet(0.1)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qiBJrKXZlP+9jtBotZndfYv2jiPaLIMrckSBToNS2mylJ7s1M94hAJg/twMqf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\nMdYa3jj07gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1277f8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "import numpy as np\n",
    "ts,rs,ps,f1s=[],[],[],[]\n",
    "for t in np.arange(0.3,0.7,0.01):\n",
    "    threshold=t\n",
    "    predict=[1 if p>threshold else 0 for p in predict_con]\n",
    "#     print accuracy_score(y_test,predict)\n",
    "    cm=confusion_matrix(y_test,predict,labels=[0,1])\n",
    "#     print cm\n",
    "    recall1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "    precision1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "    f11=2*recall1*precision1/(recall1+precision1)\n",
    "#     print threshold,recall1,precision1,f11\n",
    "    ts.append(threshold)\n",
    "    rs.append(recall1)\n",
    "    ps.append(precision1)\n",
    "    f1s.append(f11)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.plot(ts,rs,label=\"recall1\")\n",
    "plt.plot(ts,ps,label=\"precision1\")\n",
    "plt.plot(ts,f1s,label=\"f11\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.text(0.3,0.9,\"Elastic Regression(Class 1,alpha=0.1)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[175040  17982]\n",
      " [ 82941  24037]]\n",
      "recall1=0.224691057975,precision1=0.572050738951,f11=0.322650791627\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\sklearn\\discriminant_analysis.py:387: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
       "              solver='svd', store_covariance=False, tol=0.0001)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "model=LinearDiscriminantAnalysis()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[174960  18407]\n",
      " [ 82454  24179]]\n",
      "recall1=0.226749692872,precision1=0.567768750294,f11=0.324074012023\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model=GaussianNB()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[151514  41853]\n",
      " [ 66488  40145]]\n",
      "recall1=0.376478200932,precision1=0.489585111832,f11=0.425645837641\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=3,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(n_jobs=3)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[172156  21211]\n",
      " [ 62989  43644]]\n",
      "recall1=0.409291682687,precision1=0.672947344075,f11=0.509003545438\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=3,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "model=ExtraTreesClassifier(n_jobs=3)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[172887  20480]\n",
      " [ 63434  43199]]\n",
      "recall1=0.405118490524,precision1=0.678386909342,f11=0.507292498473\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model=AdaBoostClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[172637  20730]\n",
      " [ 63416  43217]]\n",
      "recall1=0.405287293802,precision1=0.675825292821,f11=0.50670653066\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model=GradientBoostingClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_con=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[179596  13771]\n",
      " [ 65391  41242]]\n",
      "recall1=0.386765822963,precision1=0.74967734899,f11=0.510275540378\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predict_con,labels=[0,1])\n",
    "print cm\n",
    "r1=float(cm[1][1])/(cm[1][0]+cm[1][1])\n",
    "p1=float(cm[1][1])/(cm[0][1]+cm[1][1])\n",
    "f11=2*r1*p1/(r1+p1)\n",
    "print \"recall1=%s,precision1=%s,f11=%s\"%(r1,p1,f11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "model=svm.SVC(n_jobs=3)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#on 244"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name MLPClassifier",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-b242a1aaecf0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mneural_network\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMLPClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mMLPClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name MLPClassifier"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "model=MLPClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
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